R probably int_conformal_cv 通過保形推理 CV+ 預測區間

int_conformal_cv(object, ...)

# S3 method for default
int_conformal_cv(object, ...)

# S3 method for resample_results
int_conformal_cv(object, ...)

# S3 method for tune_results
int_conformal_cv(object, parameters, ...)

predict.int_conformal_cv()

library(workflows)
library(dplyr)
library(parsnip)
library(rsample)
library(tune)
library(modeldata)

set.seed(2)
sim_train <- sim_regression(200)
sim_new   <- sim_regression(  5) %>% select(-outcome)

sim_rs <- vfold_cv(sim_train)

# We'll use a neural network model
mlp_spec <-
  mlp(hidden_units = 5, penalty = 0.01) %>%
  set_mode("regression")

# Use a control function that saves the predictions as well as the models.
# Consider using the butcher package in the extracts function to have smaller
# object sizes

ctrl <- control_resamples(save_pred = TRUE, extract = I)

set.seed(3)
nnet_res <-
  mlp_spec %>%
  fit_resamples(outcome ~ ., resamples = sim_rs, control = ctrl)

nnet_int_obj <- int_conformal_cv(nnet_res)
nnet_int_obj
#> Conformal inference via CV+
#> preprocessor: formula 
#> model: mlp (engine = nnet) 
#> number of models: 10 
#> training set size: 200 
#> 
#> Use `predict(object, new_data, level)` to compute prediction intervals

predict(nnet_int_obj, sim_new)
#> # A tibble: 5 × 3
#>   .pred_lower   .pred .pred_upper
#>         <dbl>   <dbl>       <dbl>
#> 1        3.67 42.8           81.9
#> 2      -29.6   9.47          48.6
#> 3      -14.6  24.5           63.6
#> 4      -39.2  -0.0793        39.0
#> 5      -11.1  28.0           67.1